Machining tools, such as CNC lathe and milling machine, etc., are now in common use over various industries. However, when a user uses a machining tool to machine a workpiece, the surface roughness of the workpiece may be influenced because of chatter generated during the machining process. Accordingly, the user needs to prevent from chatter so as to make sure that the machining stability and the machining precision can be increased.
In general, there are two major methods for detecting chatter generated by machining tools: (1) Chatter prediction method: the user needs to use an impact hammer to hit the spindle and the cutter of the machining tool in order to obtain the systematic characteristic parameters, and then draws a stability lobe diagram to select a stable spindle speed for the machining process. However, this method can only be executed after the machining tool is turned off; besides, as this method assumes the whole machining process is ideal, the systematic characteristic parameters measured by this method must be, to a certain extent, different from those measured during the machining tool is actually performing the machining process, in particular to machining impedance (workpiece impedance); (2) Chatter prevention method: when the machining tool is machining the workpiece, the users can use sensors to measure and analyze various signals generated during the machining process, and then determine whether chatter is going to occur according to the analysis result. Once the current spindle speed is identified to result in chatter occurrence, the spindle speed of the machine tool should be immediately adjusted to prevent from chatter. However, this method cannot find the best spindle speed of the machining tool, and fails to provide an objective criterion for adjusting spindle speed; also, as this method cannot completely prevent from chatter, the machining precision of the workpiece cannot be effectively improved. As described above, both conventional methods cannot make sure that workpieces can be processed in high machining precision.
Currently, there is a method developed for estimating machining process parameters. According to this method, the user should select a spindle speed adjusting range first. Then, the user should take trail cuts by keep adjusting the spindle speed within the above range, and identify the systematic dynamic parameters by operational model analysis. Next, the user should draw a stability lobe diagram according to the above parameters to find a best spindle speed within the above range. Afterward, the user should take trail cuts again according to the best spindle speed and different cutting depths, and measure and process vibration signals generated during the trail cuts to obtain vibration signal characteristics. Finally, the user should compare the vibration signal characteristics with the predetermined thresholds to determine whether chatter occurs so as to determine a best cutting depth.
However, for the machining tool, the contact area between the workpiece and the cutter, the moving path of the cutter and other operational conditions always keep changing; for the reason, the best spindle speed also keeps changing. Thus, the above method can just determine the best spindle speed for the trail cuts, but cannot properly adjust the spindle speed according to the condition of the cutter and the other operational conditions. Accordingly, the conventional method cannot effectively improve the machining precision of workpieces as well.
In addition, according to the above method, as the user should take trail cuts, but some workpieces may be extremely expensive, therefore, taking trail cuts on these workpieces will result in a lot of cost, which cannot satisfy actual requirements, so its application is restricted.
Therefore, it has become an important issue to provide a spindle speed adjusting technology to effectively improve the shortcomings of prior art.